Map Models May Predict Political Disruption

February 17, 2014

Political scientist Britt Cartrite has developed a formula that assesses voting patterns, ethnicity and geography to predict political disruption and unrest.

What if you could predict events based on ethnicity and political distinctiveness?

Britt Cartrite, an associate professor of political science at Alma College, has developed map models that can be used to explain and potentially predict political disruption and unrest.

“We’ve developed a formula that takes the votes for a political party — for example, the Communist Party — in a specific region, and compares them it to the average vote for that political party in all regions,” he says. “We do this for all political parties that contest elections. We’re looking for an average level of distinctiveness, and we do this over a number of elections and then use this date to generate maps.”

This Political Distinction Index map of Spain shows regions of low, moderate and high distinctiveness based on voting patterns. The most politically distinctive regions are places where there have been significant protests against the Spanish government, says Cartrite.

Cartrite’s map models add a critical visual element to a topic that can be unwieldy to convey with raw data. One of the interesting aspects of this type of research is that it involves connecting many data points instead of extensive fieldwork that may require significant resources.

“Votes are distinctive, but there may be other variables to consider as well, such as economic over- or under-development or distance from the capital or region,” says Cartrite. “Ultimately, I’m interested in how ethnicity explains political distinctiveness compared to other variables.”

The maps that are generated are an intuitive way to conceptualize data, he says.

“They resonate with people,” he says. “I’d like to develop a website where people could generate these maps — punch in the country, the year, and you’d get a political distinctiveness map. All the data is there; it is about implementation.”

Cartrite plans to involve students in the process. He is teaching a first-time course titled “Making and Breaking Nations,” during which students will collect data on different countries, generate maps and make observations.

“It’s scalable, and we can bring undergraduates in and use real world data,” says Cartrite. “Too often in social science we can be abstract. We’re doing real world stuff, so this is a way to get students involved in real world data and applications.”